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Distributed optimization of multi-class SVMs.

Maximilian Alber1, Julian Zimmert2, Urun Dogan3

  • 1Berlin Big Data Center, Berlin Institute of Technology, Berlin, Germany.

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Summary
This summary is machine-generated.

Distributed algorithms for all-in-one Support Vector Machines (SVMs) enable large-scale comparisons with one-vs.-rest SVMs. All-in-one SVMs demonstrate superior accuracy in text classification tasks, overcoming previous computational limitations.

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Area of Science:

  • Machine Learning
  • Computer Science
  • Data Science

Background:

  • Training one-vs.-rest Support Vector Machines (SVMs) is easily parallelizable across classes.
  • All-in-one SVMs face computational challenges due to quadratic programming complexity with increasing class numbers.

Purpose of the Study:

  • To develop distributed algorithms for two all-in-one SVM formulations.
  • To enable large-scale comparisons between all-in-one and one-vs.-rest SVMs.
  • To evaluate the performance of all-in-one SVMs on text classification.

Main Methods:

  • Developed distributed algorithms for Lee et al. and Weston and Watkins all-in-one SVM formulations.
  • Parallelized computation evenly over the number of classes.
  • Conducted large-scale comparative analysis on text classification datasets.

Main Results:

  • Achieved parallelization of all-in-one SVM training over the number of classes.
  • Enabled unprecedented scale for comparing all-in-one and one-vs.-rest SVMs.
  • Demonstrated superior accuracy of all-in-one SVMs on text classification data.

Conclusions:

  • Distributed algorithms significantly enhance the scalability of all-in-one SVMs.
  • All-in-one SVMs offer a more accurate approach for text classification compared to one-vs.-rest methods at scale.